Submersible unit and diving position control method therefor

ABSTRACT

A submersible unit having thrusters for changing a diving position based on a total work quantity, which is the sum of a first work quantity and a second work quantity. A proportional controller generates and outputs the first work quantity based on a difference between a position quantity indicating a desired target diving position and a diving position. A network controller uses a neural network data processing system to learn movement characteristics of the submersible unit based on the first work quantity and a diving position sampled over a plurality of times. The network controller generates a second work quantity using control coupling coefficients learned by minimizing an evaluation quantity determined from a difference between the learned movement characteristics and target movement characteristic values.

TECHNICAL FIELD

The present invention relates to a submersible unit and a method forcontrolling the diving position of such an unit, and especially relatesto techniques for holding constant the diving position of a submersibleunit with respect to non-periodic external disturbances.

BACKGROUND ART

Japanese Patent Application, First Publication No. Hei 7-187072discloses art relating to an automatic control method for a submersibleunit using neural networks. This automatic control method for asubmersible unit absorbs the effects of periodic external forces(external disturbances to the positional control of the submersibleunit) such as waves by using learning control employing conventionallyused proportional-plus-integral-plus-derivative control (PID control)and neural networks, thereby holding the diving position of thesubmersible unit constant even when periodic external forces areapplied. That is, according to this automatic control method, thefrequencies of periodic disturbances due to waves and the like arelearned, an oscillator network is provided for outputting a sine wavesignal of a standard frequency based on the learned frequency, and aneural controller controls the depth of the submersible unit based onthe output of the oscillator network.

However, although the above-described automatic control method for asubmersible unit is capable of holding the depth constant by absorbingthe effects of periodic external disturbances acting on the submersibleunit based on a sine wave output from an oscillator network, it is notcapable of holding the depth sufficiently constant with respect tonon-periodic external disturbances.

DISCLOSURE OF THE INVENTION

The present invention has been achieved in consideration of theabove-mentioned problems, and has the object of offering a submersibleunit and diving position control method capable of holding the divingposition of the submersible unit constant with respect to non-periodicexternal disturbances.

The present invention relating to a submersible unit comprisespropulsion means for changing a diving position based on a total workquantity which is the sum of a first work quantity and a second workquantity; proportional control means for generating and outputting saidfirst work quantity based on a difference between position quantitiesindicating a target diving position and a diving position; and networkcontrol means which uses a neural network data processing system forlearning movement characteristics of a diving position based on saidfirst work quantity and a diving position sampled over a plurality oftimes, for learning to minimize an evaluation quantity determined from adifference between said movement characteristics and target movementcharacteristic values, and setting and outputting a second work quantitybased on said movement characteristics.

In the present invention constructed in this manner, the propulsionmeans is driven based on a total work quantity obtained by adding afirst work quantity output from the proportional control means and asecond work quantity output from the network control means. In thiscase, the network control means using a neural network data processingsystem learns the movement characteristics of the submersible unit basedon the first work quantity and diving positions of the submersible unitsampled over a plurality of times, and after that learning is completed,learns to minimize an evaluation quantity comprising the differencebetween the learned movement characteristics and the target movementcharacteristic values for setting the second work quantity, which isoutput to the propulsion means.

By employing this type of structure, the present invention is capable ofholding the diving position of the submersible unit constant withrespect to non-periodic external disturbances such as waves.

The present invention relating to another submersible unit comprises anadder for adding a first work quantity and a second work quantity andoutputting a total work quantity; propulsion means for changing a divingposition based on said total work quantity; position detection means fordetecting said diving position and outputting a position quantity;target position setting means for setting and outputting a target divingposition value; target movement value setting mans for setting andoutputting a target change velocity value and a target changeacceleration value for the diving position; a subtracter for subtractingsaid position quantity from target position value and outputting aposition error quantity; proportional control means for performing aproportional-plus-integral-plus-derivative operation on said positionerror quantity and outputting said first work quantity; first networkcontrol means which uses a neural network data processing system, forreceiving as inputs a current position change quantity which is saidtarget position value subtracted from said position quantity for acurrent time, a past position change quantity which is said targetposition value subtracted from said position quantity for a past time,and said total work quantity, for multiplying predetermined estimatedcoupling coefficients with the inputs and outputting an estimated changevelocity and estimated change acceleration of the diving position for afuture time, for learning settings of said estimated couplingcoefficients by minimizing evaluation quantities comprising a differencebetween said estimated change velocity and a change velocity of thediving position for a current time, and a difference between saidestimated change acceleration and a change acceleration of the divingposition for a current time, and for outputting error signals comprisinga difference between a change velocity of the diving position determinedfrom said position quantity and said target change velocity value, and adifference between a change acceleration of the diving positiondetermined from said position quantity and a target change accelerationvalue; and second network control means which uses a neural network dataprocessing system, for receiving as inputs said change velocity for acurrent time and a past time, for outputting said second work quantityby multiplying a predetermined control coupling coefficient with each ofsaid inputs, and learning settings of said control coupling coefficientby minimizing said error signal.

In the present invention constructed in this manner, the adder adds thefirst work quantity and the second work quantity, and outputs a totalwork quantity. The propulsion means changes the diving position of thesubmersible unit based on the total work quantity input from the adder.The position detection means detects the diving position of thesubmersible unit and outputs a position quantity. The target positionvalue setting means sets and outputs the target diving position value.

The target movement value setting means sets and outputs the targetchange velocity and the target change acceleration of the divingposition. The subtracter subtracts the position quantity detected by theposition detection means from the target position value set by thetarget position value setting means, and outputs a position errorquantity. The proportional control means performs aproportional-plus-integral-plus-derivative operation on the positionerror quantity input from the subtracter, and outputs a first workquantity.

The first network control means uses a neural network data processingsystem, receives as inputs a current position change quantity which isobtained by subtracting the target position value set by the targetposition value setting means from the position quantity for a currenttime detected by the position detection means, a past position changequantity of a past time which is obtained by delaying the currentposition change quantity, and a total work quantity input from theadder, multiplies predetermined estimated coupling coefficients with theinputs and outputs an estimated change velocity and estimated changeacceleration of the diving position for a future time, learns settingsof the estimated coupling coefficients by minimizing evaluationquantities comprising a difference between the estimated change velocityand a change velocity of the diving position for a current time obtainedby differentiating position quantities detected by the positiondetection means, and a difference between the estimated changeacceleration and a change acceleration of the diving position for acurrent time obtained by differentiating the change velocity, andoutputs error signals comprising a difference between a change velocityof the diving position obtained by differentiating the position quantityafter the learning and the target change velocity value set by thetarget movement value setting means, and a difference between a changeacceleration of the diving position obtained by differentiating thechange velocity and the target change acceleration value set by thetarget movement value setting means.

The second network control means uses a neural network data processingsystem, receives as inputs the change velocity for a current time and apast time obtained by differentiating the position quantity detected bythe position detection means, outputs the second work quantity bymultiplying a predetermined control coupling coefficient with each ofthe inputs, and learns settings of the control coupling coefficient byminimizing the error signal.

On the other hand, the present invention relating to a diving positioncontrol method for a submersible unit, comprises steps of learningmovement characteristics of a submersible unit while the submersibleunit is proportionally controlled based on a first work quantitygenerated from a difference between an actual diving position and atarget diving position value, based on differences between said firstwork quantity and said target position value, and diving positionssampled over a plurality of times; generating an error signal comprisinga difference between said movement characteristics and target values ofsaid movement characteristics; and generating a second work quantitybased on said movement characteristics for addition to said first workquantity, and learning to generate the second work quantity based onsaid movement characteristics such as to minimize said error signal.

By employing a diving position control method of this type, it ispossible to hold the diving position of a submersible unit constant withrespect to non-periodic external disturbances such as waves.

Additionally, the present invention relating to another diving positioncontrol method for a submersible unit comprises steps of learningmovement characteristics of a submersible unit while the submersibleunit is proportionally controlled based on a first work quantitygenerated from a difference between an actual diving position and atarget diving position value, based on differences in diving positionssampled with respect to said first work quantity and said targetposition value for a current time, a first past time which is apredetermined unit time period earlier than said current time, and asecond past time which is said predetermined unit time period earlierthan said first past time; generating an error signal comprising adifference between a change velocity of the diving position and a targetchange velocity value with respect to said change velocity, and adifference between a change acceleration of the diving position and atarget change acceleration value with respect to said changeacceleration; and generating a second work quantity based on the changevelocities for said current time, the first past time and the secondpast time for addition to said first work quantity, and learning togenerate the second work quantity based on said movement characteristicssuch as to minimize said error signal.

By employing a diving position control method of this type, it ispossible to hold the diving position of a submersible unit constant withrespect to non-periodic external disturbances such as waves.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are supplemented to the explanation of the bestmodes for carrying out the invention described below in order to give abetter understanding of the present invention. That is:

FIG. 1A is a side view showing an embodiment of a submersible unit inthe submersible unit and diving position control method thereofaccording to the present invention.

FIG. 1B is a plan view showing an embodiment of a submersible unit inthe submersible unit and diving position control method thereofaccording to the present invention.

FIG. 2 is a block diagram showing an embodiment of a submersible unitand a diving position control method thereof according to the presentinvention.

FIG. 3A is a structural diagram showing a forward model networkaccording to an embodiment of a network control means in a submersibleunit and a diving position control method thereof according to thepresent invention.

FIG. 3B is a structural diagram showing a controller network accordingto an embodiment of a network control means in a submersible unit and adiving position control method thereof according to the presentinvention.

FIG. 4 is a block diagram showing a control system of a submersible unitduring a learning period of a forward model network in a submersibleunit and diving position control method thereof according to the presentinvention.

FIG. 5 is a block diagram showing a control system of a submersible unitafter a learning period of a forward model network in a submersible unitand diving position control method thereof according to the presentinvention.

FIG. 6 is a diagram for explaining the functions of a network controlmeans in a submersible unit and diving position control method thereofaccording to the present invention.

BEST MODES FOR CARRYING OUT THE INVENTION

Hereinbelow, the best mode for carrying out the present invention shallbe explained with reference to the drawings. First, the outer structureof the submersible unit which is the subject of control in the presentembodiment shall be described with reference to FIGS. 1A and 1B. In thisdrawing, reference numeral 1 denotes a submersible unit which navigatesunderwater in the ocean or the like, and is connected to a mother shipanchored on the ocean surface by means of a cable B. The submersibleunit 1 is supplied with electrical power from the mother ship throughthe cable B and receives various types of command signals for underwaternavigation. The submersible unit 1 performs various types of underwaterwork based on the electrical power and commands signals supplied fromthe mother ship in this manner.

As a propulsion means for propelling the submersible unit 1, thesubmersible unit 1 is provided with three thrusters 2 for propelling thesubmersible unit in an up/down direction, i.e. in a depth direction, twothrusters 3 for propelling the submersible unit 1 in a forward/reversedirection, and one thruster 4 for turning the submersible unit in aleft/right direction. Each of these thrusters 2, 3 and 4 is driven by amotor 5 capable of rotating both clockwise and counter-clockwise.Additionally, a depth sensor 6 for detecting the diving depth isprovided at the front of an upper portion of the submersible unit 1, anda pinger 7 for generating sounds undersea is provided at the center ofthe upper portion. The mother ship detects the position of the divingunit 1 by measuring the sound generated by the pinger 7 at three points.

Additionally, the submersible unit 1 has a TV camera or the like, andsends work images taken by the TV camera to the mother ship via thecable B. In the mother ship, an operator outputs command signals to thesubmersible unit 1 relating to various types of undersea work based onthe work images.

If, for example, a submersible unit 1 of this type of structure isworking close to the ocean surface, non-periodic up/down movements orlateral sway in the forward/reverse and left/right directions due to theinfluence of ocean currents or undulations based on waves formed on theocean surface can be applied to the submersible unit 1 as externaldisturbances.

Next, a control structure for the diving depth of the above-describedsubmersible unit 1 shall be explained with reference to FIG. 2. In thedrawing, reference numeral 10 denotes a target depth value settingmeans. This target depth value setting means 10 sets a target depthvalue R_(S) which is a target value for the diving position of thesubmersible unit 1, and outputs this target depth value R_(S) to asubtracter 11 and a network control means 12 to be described later. Thesubtracter 11 generates a depth error signal G(t) by subtracting a depthquantity (position quantity) Z(t) indicating the diving depth of thesubmersible unit 1 at time t, in other words the output of the depthsensor 6, from the target depth value R_(S), and outputs this deptherror signal G(t) to a PID controller (proportional control means) 13.

The PID controller 13 samples the depth error signal G(t) everypredetermined unit period of time, and outputs a work quantity U_(pid)(t) indicating a number of rotations of the motor 5 driving the thruster2, in other words a first work quantity, based on the sampled valueobtained by sampling, and outputs this sampled value to an adder 14. Theadder 14 adds work quantity U_(pid) (t) to a work quantity U_(nn) (t)input from the network control means 12 to be described later, in otherwords a second work quantity, and outputs this addition value to thethruster 2 and the network control means 12 as a total work quantity U₀(t).

The thruster 2 is driven based on this total work quantity U₀ (t) tochange the diving depth of the submersible unit 1. Simultaneously, thedepth sensor 6 detects the change in diving depth of the submersibleunit 1, and outputs the diving quantity Z(t) to the adder 11 and thenetwork control means 12. The network control means 12 is a controlmeans which performs neural network type information processing anddetermines input and output characteristics by standard learning.

Additionally, reference numeral 20 denotes a subtracter. This subtracter20 subtracts the target depth value R_(S) from the depth quantity Z(t)to calculate a current depth change quantity H(t), in other words acurrent position change quantity, which is output to a delay means (D)22 and a forward model network 21 which is a first network controlmeans. The delay means 22 outputs the current depth change quantity H(t)to the forward model network 21 and a delay means (D) 23 after a delayof a unit period of time Δt. The delay means 23 forwards the output ofthe delay means 22 to the forward model network 21 after a delay of aunit period of time Δt.

That is, the forward model network 21 receives as inputs the currentdepth change quantity H(t) of time t from the subtracter 20, andsimultaneously a past depth change quantity H(t-Δt) of a time (t-Δt)which is a unit period of time Δt earlier than the current time t, inother words a first past time, from the delay means 22. Additionally,the forward model network 21 also receives as an input a past depthchange quantity H(t-2Δt) of a time (t-2Δt) which is a unit period oftime Δt earlier than the time (t-Δt), in other words a second past time,from the delay means 23.

Next, the structure of the forward model network 21 shall be explainedin detail with reference to FIG. 3A. As shown in this drawing, theforward model network 21 is a layered network composed of an input layerl, a middle layer m and an output layer n.

The input layer l is composed of four nodes, for example, of which thetotal work quantity U₀ is input into the first node, the current depthchange quantity H(t) is input into the second node, the past depthchange quantity H(t-Δt) is input into the third node, and the past depthchange quantity H(t-2Δt) is input into the fourth node.

The middle layer m is composed of six nodes. The number of nodes in thismiddle layer m is set such as to enable the forward model network 21 toappropriately express the movement characteristics of the submersibleunit 1. Each node in the middle layer m receives as inputs quantitiesobtained by multiplying a predetermined coupling coefficient, in otherwords an estimated coupling weight, with the total work quantity U₀, thecurrent depth change quantity H(t), and the past depth change quantitiesH(t-Δt) and H(t-2Δt) input to the input layer l. Each node in the middlelayer m processes a variable x obtained by summing the quantities inputfrom each node in the input layer l using the threshold value functionshown below, and outputs the results to the output layer n.

    f(x)=1/{1+exp(-5x)}-0.5                                    (1)

The output layer n is composed of two nodes. The output of each node inthe middle layer m multiplied by the predetermined coupling coefficient,in other words the estimated coupling weight, is input into each node inthe output layer n. The first node in the output layer n applies theabove-given threshold function (1) to a quantity obtained by summing theinput quantities, and outputs to subtracter 24 an estimated quantity ofthe diving depth change velocity of the submersible unit 1 at a time(t+Δt) which is a unit period of time Δt after the current time t, inother words an estimated change velocity V(t+Δt). The second node of theoutput layer n applies the above-given threshold function (1) to aquantity obtained by summing the input quantities, and outputs tosubtracter 24 an estimated quantity of the diving depth changeacceleration of the submersible unit 1 at the time (t+Δt), in otherwords an estimated change acceleration A(t+Δt).

Reference numerals 26 and 27 denote differentiation means (S). Thedifferentiation means 26 calculates a change velocity V(t) of the divingposition of the submersible unit 1 at the current time t bydifferentiating the depth quantity Z(t), and outputs the change velocityV(t) to the differentiation means 27 and the subtracter 24 andsubtracter 28 respectively. The differentiation means 27 calculates achange acceleration A(t) of the diving position of the submersible unit1 by differentiating the change velocity V(t), and outputs the changeacceleration A(t) to the subtracter 25 and the subtracter 29.

Here, the change velocity and change acceleration of the submersibleunit 1 with regard to the diving depth are quantities expressing themovement characteristics of the submersible unit 1.

The subtracter 24 subtracts the estimated change velocity V(t+Δt) fromthe change velocity V(t) and outputs the result to the forward modelnetwork 21. The subtracter 25 subtracts the change acceleration A(t+Δt)from the change acceleration A(t), and outputs the result to the forwardmodel network 21. Reference numeral 30 denotes a target movement valuesetting means which sets a target change velocity value R_(v) of thediving depth which is output to the subtracter 28, and sets a targetchange acceleration value R_(a) which is output to the subtracter 29.

The subtracter 28 subtracts the actual change velocity V(t) at thecurrent time t from the target change velocity value R_(v) of thesubmersible unit 1 at the diving depth, and outputs the result to theforward model network 21. The subtracter 29 subtracts the actual changeacceleration A(t) at the current time t from the target changeacceleration value R_(a) of the submersible unit 1 at the diving depth,and outputs the result to the forward model network 21.

The outputs of the subtracters 24 and 25 are used by the forward modelnetwork 21 in order to learn the settings of the coupling coefficients.Additionally, the forward model network 21 calculates an error signal byusing an error E₁ (t) determined by the following evaluation formulabased on the outputs of the subtracters 28 and 29, and outputs theresults to the controller network 31, in other words the second networkcontrol means.

    E.sub.1 (t)=0.5{(R.sub.v -V(t)).sup.2 +(R.sub.a -A(t)).sup.2 }(2)

The forward model network 21 learns the settings of the couplingcoefficients in accordance with learning algorithms of a conventionalreverse error propagation method.

Furthermore, the change velocity V(t) of the diving depth at currenttime t is input into the controller network 31 and the delay means (D)32. The delay means 32 delays the change velocity V(t) by a unit periodof time Δt and outputs the result to the controller network 31 and thedelay means (D) 33. The delay means 33 delays the output of the delaymeans 32 by a unit period of time Δt and outputs the result to thecontroller network 31.

That is, the change velocity V(t) of the current time t is input fromthe differentiation means 26 to the controller network 31. Additionally,at the same time, a change velocity V(t-Δt) of a time (t-Δt) which is aunit period of time Δt prior to the current time t is input to thecontroller network 31 from the delay means 32, and a change velocityV(t-2Δt) of a time (t-2Δt) which is a unit period of time Δt prior tothe time (t-Δt) is input to the controller network 31 from the delaymeans 33.

Next, the structure of the controller network 31 shall be explained indetail with reference to FIG. 3B. As shown in this drawing, thecontroller network 31 is composed of an input layer i, a middle layer jand an output layer k which form a layered network having three layers.The input layer i is composed of three nodes, among which the changevelocity V(t) is input into the first node, the change velocity V(t-Δt)is input into the second node, and the change velocity V(t-2Δt) is inputinto the third node; the outputs of all of the nodes in the input layeri are input into each node in the middle layer j.

The middle layer j is composed of six nodes. Each node in the middlelayer j receives as inputs quantities obtained by multiplying apredetermined coupling coefficient with the change velocity V(t) of thecurrent time t, the change velocity V(t-Δt) of a past time (t-Δt), andthe change velocity V(t-2Δt) of a time further in the past (t-2Δt). Eachnode in the middle layer j processes a quantity obtained by summing thequantities input into the nodes using the above-given threshold valuefunction (1), and outputs the results to the output layer k.

The output layer k is composed of a single node. The outputs of therespective nodes of the middle layer j, multiplied with predeterminedcoupling coefficients, in other words control coupling weights, areinput into the node of the output layer k. The node of this output layerk generates the work quantity U_(nn), in other words the second workquantity for the current time t, by applying the threshold valuefunction (1) to the quantity obtained by summing the input quantities,and outputs this to the adder 14. The coupling coefficients in thecontroller network 31 are set by learning in accordance with learningalgorithms of a conventional reverse error propagation method in thesame manner as the forward model network 21.

Next, the diving position control operations for the submersible unit 1having the above-described structure shall be explained in detail.

In this case, the coupling coefficients of the forward model network 21and the controller network 31 are initially set at random and extremelysmall values. Therefore, the forward model network 21 does not output anerror signal to the controller network 31 until the couplingcoefficients are set to their optimum conditions after learning for astandard period of time. Additionally, since the coupling coefficientsin the controller network 31 are also set at random and extremely smallinitial values, only an extremely small value is output as the workquantity U_(nn) in the learning period of the forward model network 21.

Accordingly, when a random external disturbance such as a wave or thelike is applied to the submersible unit 1, the thruster 2 is controlledexclusively by the PID controller 13 over a standard period of time fromwhen control is initiated until the forward model network 21 has gainedsome level of learning.

The control system of the submersible unit 1 during the learning periodof the forward model network 21 is a control system such as shown inFIG. 4. Each coupling coefficient of the forward model network 21 duringthis period is learned such that the evaluation quantity E₂ (t)calculated by the following evaluation formula gives a minimum value,based on the values input from the subtracters 24, 25.

    E.sub.2 (t)=0.5{(V(t+Δt)-V(t)).sup.2 +{A(t+Δt)-A(t)).sup.2 }(3)

That is, in the depth direction, the coupling coefficients are learnedand set by taking the minimum value of the difference between the actualchange velocity V(t) of the submersible unit 1 at the current time t andthe estimated change velocity V(t+Δt) for a time (t+Δt) which is a unitperiod of time Δt after the current time t, and the couplingcoefficients are learned and set by taking the minimum value of thedifference between the actual change acceleration A(t) of thesubmersible unit 1 at the current time t and the estimated changeacceleration A(t+Δt) for a time (t+Δt) which is a unit period of time Δtafter the current time t.

As a result, the estimated change velocity V(t+Δt) and the estimatedchange acceleration A(t+Δt) estimated and output by the forward modelnetwork 21 become close in value to the change velocity V(t) and changeacceleration A(t) which express the actual movement characteristics ofthe submersible unit. Simultaneously, the forward model network 21learns the movement characteristics of the submersible unit when arandom external disturbance has been applied, thereby modeling themovement characteristics of the submersible unit 1.

As the coupling coefficients are gradually set to the optimum values dueto this type of learning, the forward model network 21 begins to outputthe error signals to the controller network 31.

Next, after the forward model network 21 has completed learning, thediving position control system of the submersible unit 1 becomes asshown in FIG. 5. Hereinbelow, the diving position control operations ofthe submersible unit 1 in this state shall be explained in detail withreference to FIG. 5.

When error signals generated based on the errors E₁ (t) expressed by theabove evaluation formula (2) are input from the forward model network21, the coupling coefficients of the controller network 31 are learnedby taking the minimum values for the errors E₁ (t). The learning periodof the controller network 31 differs from the learning period of theforward model network in that the work quantity U_(nn) (t) is applied tothe adder 14 as a larger value as the learning progresses and thecoupling coefficients are optimized. That is, during this learningperiod, the controller network 31 learns while controlling the thruster2 based on the sum of the work quantity U_(nn) (t) output from thecontroller network 31 and the work quantity output from the PIDcontroller 13.

In this case, the coupling coefficients of the controller network 31 arelearned and set such that the difference between the target changevelocity value R_(v) in the depth direction and the actual changevelocity V(t) of the submersible unit 1 is a minimum value, and suchthat the target change acceleration value R_(a) in the depth directionand the actual change acceleration A(t) is a minimum value.

As a result, the work quantity U_(nn) (t) is set such that the changevelocity V(t) which indicates the actual movement characteristics of thesubmersible is a value close to the target change velocity value R_(v),and the change acceleration A(t) is a value close to the targetacceleration value R_(a), in other words such as to suppress depthchanges of the submersible unit 1 based on the change velocities V(t),V(t-Δt) and V(t-2Δt) which indicate the actual movement characteristicsof the submersible unit 1.

FIG. 6 is a graph showing the mean-squared error characteristics a ofthe depth change of the submersible unit 1 when the thruster 2 iscontrolled by the above-described network control means 12 and a PIDcontroller 13, and the mean-squared error characteristics b when thethruster 2 is controlled by only a PID controller 13, shown with respectto the elapsed time from control initiation. As can be seen from thisgraph, there is almost no difference between the mean-squared error aand the mean-squared error b until 300 seconds after initiation, but asthe network control means 12 begins to learn and outputs the workquantity U_(nn), the mean-squared error characteristic a becomes to havea smaller value than the mean-squared error characteristic b, and theeffects of the network control means 12 appear dramatically.

While the control of the diving depth of the submersible unit 1, inother words the control of the thruster 2 has been described for thepurposes of the above embodiment, the above-described position controlmethod can also be used to control the forward/reverse direction or theright/left direction, in other words to control thruster 3 or thruster4, by detecting the position of the submersible unit 1 in theforward/reverse direction or in the right/left direction.

We claim:
 1. A submersible unit, comprising:propulsion means forchanging a diving position based on a total work quantity which is thesum of a first work quantity and a second work quantity; proportionalcontrol means for generating and outputting said first work quantitybased on a difference between position quantities indicating a targetdiving position and a diving position; and network control means whichuses a neural network data processing system for learning movementcharacteristics of said submersible unit based on said first workquantity and a diving position sampled over a plurality of times, forlearning to minimize an evaluation quantity determined from a differencebetween said movement characteristics based on the speed of displacementof said diving position sampled over a plurality of times and targetmovement characteristic values, and outputting a second work quantity.2. A submersible unit as recited in claim 1, wherein the movementcharacteristics are obtained by a change velocity and changeacceleration of the diving position.
 3. A submersible unit as recited inclaim 2, wherein the network control means comprises a layered networkcomposed of at least three layers.
 4. A submersible unit as recited inclaim 2, wherein the plurality of times include at least a current time,a first past time which is a predetermined unit time period earlier thansaid current time, and a second past time which is said predeterminedunit time period earlier than said first past time.
 5. A submersibleunit as recited in claim 1, wherein the network control means comprisesa layered network composed of at least three layers.
 6. A submersibleunit as recited in claim 5, wherein the plurality of times include atleast a current time, a first past time which is a predetermined unittime period earlier than said current time, and a second past time whichis said predetermined unit time period earlier than said first pasttime.
 7. A submersible unit as recited in claim 1, wherein the pluralityof times include at least a current time, a first past time which is apredetermined time period earlier than said current time, and a secondpast time which is said predetermined time period earlier than saidfirst past time.
 8. A submersible unit, comprising:an adder for adding afirst work quantity and a second work quantity and outputting a totalwork quantity; propulsion means for changing a diving position based onsaid total work quantity; position detection means for detecting saiddiving position and outputting a position quantity; target positionsetting means for setting and outputting a target diving position value;target movement value setting mans for setting and outputting a targetchange velocity value and a target change acceleration value for thediving position; a subtracter for subtracting said position quantityfrom target position value and outputting a position error quantity;proportional control means for performing aproportional-plus-integral-plus-derivative operation on said positionerror quantity and outputting said first work quantity; first networkcontrol means which uses a neural network data processing system, forreceiving as inputs a current position change quantity which is saidtarget position value subtracted from said position quantity for acurrent time, a past position change quantity which is said targetposition value subtracted from said position quantity for a past time,and said total work quantity, for multiplying predetermined estimatedcoupling coefficients with the inputs and outputting an estimated changevelocity and estimated change acceleration of the diving position for afuture time, for learning settings of said estimated couplingcoefficients by minimizing evaluation quantities comprising a differencebetween said estimated change velocity and a change velocity of thediving position for a current time, and a difference between saidestimated change acceleration and a change acceleration of the divingposition for a current time, and for outputting error signals comprisinga difference between said target change velocity value and a changevelocity of the diving position determined from said position quantity,and a difference between said target change acceleration value and achange acceleration of the diving position determined from said changevelocity; and second network control means which uses a neural networkdata processing system, for receiving as inputs said change velocity fora current time and a past time, for outputting said second work quantityby multiplying a predetermined control coupling coefficient with each ofsaid inputs, and learning settings of said control coupling coefficientby minimizing said error signal.
 9. A submersible unit as recited inclaim 8, wherein the diving position is a diving depth.
 10. A divingposition control method for a submersible unit, comprising stepsof:learning movement characteristics of a submersible unit while thesubmersible unit is proportionally controlled based on a first workquantity generated from a difference between an actual diving positionand a target diving position value, based on differences between saidfirst work quantity and said target position value, and diving positionssampled over a plurality of times; generating an error signal comprisinga difference between said movement characteristics and target values ofsaid movement characteristics; and generating a second work quantitybased on said movement characteristics for addition to said first workquantity, and learning to generate the second work quantity based onsaid movement characteristics such as to minimize said error signal. 11.A diving position control method for a submersible unit as recited inclaim 10, wherein the movement characteristics are obtained by a changevelocity and change acceleration of the diving position.
 12. A positioncontrol method for a submersible unit as recited in claim 11, whereinthe learning of the movement characteristics of the submersible unit andthe learning of the generation of the second work quantity is performedby a data processing means based on neural networks.
 13. A positioncontrol method for a submersible unit as recited in claim 12, whereinthe diving position is a diving depth.
 14. A position control method fora submersible unit as recited in claim 11, wherein the diving positionis a diving depth.
 15. A position control method for a submersible unitas recited in claim 10, wherein the learning of the movementcharacteristics of the submersible unit and the learning of thegeneration of the second work quantity is performed by a data processingmeans based on neural networks.
 16. A position control method for asubmersible unit as recited in claim 15, wherein the diving position isa diving depth.
 17. A position control method for a submersible unit asrecited in claim 10, wherein the diving position is a diving depth. 18.A diving position control method for a submersible unit, comprisingsteps of:learning movement characteristics of a submersible unit whilethe submersible unit is proportionally controlled based on a first workquantity generated from a difference between an actual diving positionand a target diving position value, based on differences in divingpositions sampled with respect to said first work quantity and saidtarget position value for a current time, a first past time which is apredetermined unit time period earlier than said current time, and asecond past time which is said predetermined unit time period earlierthan said first past time; generating an error signal comprising adifference between a change velocity of the diving position and a targetchange velocity value with respect to said change velocity, and adifference between a change acceleration of the diving position and atarget change acceleration value with respect to said changeacceleration; and generating a second work quantity based on the changevelocities for said current time, the first past time and the secondpast time for addition to said first work quantity, and learning togenerate the second work quantity based on said movement characteristicssuch as to minimize said error signal.
 19. A position control method fora submersible unit as recited in claim 18, wherein the learning of themovement characteristics of the submersible unit and the learning of thegeneration of the second work quantity is performed by a data processingmeans based on neural networks.
 20. A position control method for asubmersible unit as recited in claim 18, wherein the diving position isa diving depth.